This is an abridged version of the results. For more details of the LASSO models and interactive maps showing how coefficients for different variables vary spatially, please download Full_Results.html here and open it with your browser.
Are people more likely to connect online with others who share their political opinions? Do people unfriend those with an opposing political orientation? The relationship between political attitudes and social networks has been extensively examined in the literature of political science, sociology, and communication, mostly at the individual level. Scholars tend to approach political homophily as an outcome of individual psychological/behavioral inclinations. However, social networks are reflections of our lifeworlds, and politics are always grounded in local contexts. Local socio-political environment can cast an influence people's online connections, but such ecosystem variables are less explored by existing research.
Focusing on county-level social media connection allows us to bring in contextual variables to test factors that are positively/negatively associated with cross-cutting social connections. Geographically weighted regression further allows us to explore the variation of these relationships across space, achieving a nuanced depiction of factors influencing cross-cutting connections in counties all over the U.S.
for index, row in format_df.iterrows(): p = make_plot(format_df, row['field'], "Physical", 'dv') show(p)Cross-cutting connection rate on Facebook: a county’s ratio of cross-cutting to likeminded Facebook friends with other counties.
Cross-cutting connection rate based on human mobility data: a county’s ratio of cross-cutting to likeminded physical travel to other counties.
$$CrossCuttingConnectivityRate_{i} = \sum \limits _{j=0} ^{n-1} \frac{1 - (DemRatio_{i} DemRatio_{j} + GOPRatio_{i} GOPRatio_{j})}{DemRatio_{i} DemRatio_{j} + GOPRatio_{i} GOPRatio_{j}} * Connectivity_{ij}$$